Verbal language analysis

ABSTRACT

Verbal language analysis is provided to users. The user enrolls or subscribes for verbal language analysis or analytics. The user carries out or conducts a conversation with a third party. An intelligence device associated with the user records the conversation. The intelligence device performs verbal language analysis on the conversation. The verbal language analysis generates individual metrics for verbal factors of energy, word count, inflection, tone (e.g. pitch and sentiment), rate, and/or the like. A verbal intelligence index is determined from the individual metrics using aggregation, averaging, weighted averaging, and/or the like. An interface component generates views to display to the user for review of the conversation to facilitate better verbal performance during current and in future conversations.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional PatentApplication Ser. No. 62/984,655, entitled “VERBAL LANGUAGE ANALYSIS”filed on Mar. 3, 2020. The entirety of the above-noted application isincorporated by reference herein.

BACKGROUND

Currently, verbal conversation analytics are limited in theireffectiveness because they do not incorporate Verbal Intelligence (VI)into analytics being produced. The lacking negatively impacts thesuccess of Call Centers and Customer Service Representatives throughoutthe world, as well as the management effectiveness in many differenttypes of organizations. A low level of VI negatively affects allinterpersonal relationships.

The effectiveness of telephonic business communication is limited bycertain neurological responses that potentially limit the amount oftrust the communicating individuals have with one another. While theissues and solutions may be articulated during a call, the intendedoutcome may not materialize due to certain regions of the brain“freezing up” or a sub-optimal presentation of the issues/solutionsduring a conversation (e.g. stuttering, stammering, repetition, longpauses, “um's,” “ah's” and/or the like. The result is reduced sales,reduced customer service, increased training, increased turnover and/orhiring costs.

Current solutions are only directed towards the “tracking” ofcalls/conversation or improving the “mechanics” of thecall/conversation. The current call analytic solutions measure thingssuch as: Length of call, Talking vs. listening time, and Questions vs.answers time.

The “mechanical” solutions focus on making the customer servicerepresentative (CSR) or sales processes more economical and efficientwith tools such as: Call forwarding and cueing, Call transcripts,Virtual and remote call answering, and Bots. However, none of thesemechanical solutions address increasing the personal effectiveness of auser/conversant.

Verbal Intelligence has been studied by various entities.Neuroscientists around the world have been studying the role the brainplays in the course of a conversation. Conversations impact theneurochemistry of the brain. There are factors that result in “good”conversations and “bad” conversations. However, the conversationalistcan be completely unaware of many of the factors. In good conversations,people know where they stand with others—they feel safe. Researchindicates that trust is considered the number one trait of feeling safeand a good conversation. In terms of importance, people allocate 7% towords, 38% to tone of voice, and 55% to nonverbal behaviors of in personconversations.

BRIEF SUMMARY OF THE DESCRIPTION

The following presents a simplified summary of the innovation in orderto provide a basic understanding of some aspects of the innovation. Thissummary is not an extensive overview of the innovation. It is notintended to identify key/critical elements of the innovation or todelineate the scope of the innovation. Its sole purpose is to presentsome concepts of the innovation in a simplified form as a prelude to themore detailed description that is presented later.

Verbal language analysis is provided to users. The user enrolls orsubscribes for verbal language analysis or analytics. The user carriesout or conducts a conversation with a third party. An intelligencedevice associated with the user records the conversation. Theintelligence device performs verbal language analysis on theconversation. The verbal language analysis generates individual metricsfor verbal factors of energy, word count, inflection, tone (e.g. pitchand sentiment), rate, and/or the like. A verbal intelligence index isdetermined from the individual metrics using aggregation, averaging,weighted averaging, and/or the like. An interface component generatesviews to display to the user for review of the conversation tofacilitate better verbal performance in future conversations.

In aspects, the subject innovation provides substantial benefits interms of verbal language analysis. One advantage resides in a providingreal-time or near real time metrics and views for a user to increaseeffectiveness in conversations. Another advantage resides in anobjective metric to determine effectiveness of conversations.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the innovation are described herein inconnection with the following description and the annexed drawings.These aspects are indicative, however, of but a few of the various waysin which the principles of the innovation can be employed and thesubject innovation is intended to include all such aspects and theirequivalents. Other advantages and novel features of the innovation willbecome apparent from the following detailed description of theinnovation when considered in conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Aspects of the disclosure are understood from the following detaileddescription when read with the accompanying drawings. It will beappreciated that elements, structures, etc. of the drawings are notnecessarily drawn to scale. Accordingly, the dimensions of the same maybe arbitrarily increased or reduced for clarity of discussion, forexample.

FIG. 1 illustrates a system diagram for verbal language analysis.

FIG. 2 illustrates an example component diagram of the intelligencedevice 120.

FIG. 3 illustrates an example call message structure.

FIG. 4 illustrates a call tag structure part of the call messagestructure.

FIG. 5 illustrates a caller tag structure part of the call messagestructure.

FIG. 6 illustrates a recipient tag structure part of the call messagestructure.

FIG. 7 illustrates a pitch track view and analysis of deviations from abaseline pitch to determine sentiment.

FIG. 8 illustrates a synchronization of the pitch track and the speechsegment.

FIG. 9 illustrates an exemplary embodiment of a live view or real timedashboard generated on a computer screen.

FIG. 10 illustrates an exemplary embodiment of a wearable device as acapture component that is wirelessly connected to a user's mobile phone.

FIG. 11 illustrates an exemplary embodiment of a VI index meter view.

FIG. 12 illustrates a method for verbal language analysis.

FIG. 13 illustrates a computing environment where one or more of theprovisions set forth herein can be implemented, according to someembodiments.

DETAILED DESCRIPTION

Verbal language analysis is provided to users. The user enrolls orsubscribes for verbal language analysis or analytics. The user carriesout or conducts a conversation with a third party. An intelligencedevice associated with the user records the conversation. Theintelligence device performs verbal language analysis on theconversation. The verbal language analysis generates individual metricsfor verbal factors of energy (volume), word count, inflection, tone(e.g. pitch and sentiment), rate, and/or the like. The verbal languageanalysis may be performed in real time or near real time. A verbalintelligence index is determined from the individual metrics usingaggregation, averaging, weighted averaging, and/or the like. Aninterface component generates views to display to the user for review ofthe conversation to facilitate better verbal performance during thecurrent and in future conversations.

Various aspects of the subject disclosure are now described in moredetail with reference to the annexed drawings, wherein like numeralsgenerally refer to like or corresponding elements throughout. It shouldbe understood, however, that the drawings and detailed descriptionrelating thereto are not intended to limit the claimed subject matter tothe particular form disclosed. Rather, the intention is to cover allmodifications, equivalents, and alternatives falling within the spiritand scope of the claimed subject matter.

FIG. 1 illustrates a system 100 for intelligent verbal analytics. Thesystem 100 includes a user 105 enrolled or registered for verballanguage analysis. The user 105 enrolls for the service to monitor oneor more conversations 110 with one or more third parties 115. It isappreciated that third party and recipient are synonymous for purposesof this application and are used interchangeably herein. Theconversation 110 may be conducted in person, digitally, electronically,telephonically, voice over internet protocol (VOIP), and/or the like. Insome embodiments, the conversation 110 can be a speech, presentation,pitch, and/or the like directed at multiple third parties 115.

The system 100 includes an intelligence device 120. The intelligencedevice 120 is associated with the user 105. The intelligence device 120can be a wearable device, mobile device, microphone, AI device, internetof things (IOT) device, and/or the like. For example, the intelligencedevice 120 is a mobile phone that includes a microphone or otherrecording apparatus. In another example, the intelligence device 120 isa wearable IOT device such as a ring, a necklace, glasses, and/or thelike that includes a microphone or other recording apparatus.

In some embodiments, the intelligence device 120 can be available as amobile application (app), as an enterprise solution, a wearable, and/orthe like. As a mobile application, the intelligence device 120 can beavailable in a mobile application formats and digital applicationstores. The intelligence device 120 can utilize an applicationprogramming interface (API) so it can integrate into other solutions toseamlessly share data and/or the like. In some embodiments, theintelligence device 120 can use or subscribe to a web service or similarcloud solution to store and retrieve data.

The intelligence device 120 can include a microphone and/or otherrecording apparatus. The intelligence device 120 can record (or receivean audio stream for real time processing) the conversation 110 betweenthe user 105 and the third party 115. The intelligence device 120 canstore the recording of the conversation in a local storage apparatus,remote or cloud solution, and/or the like. In some embodiments, theintelligence device 120 can be an integration of other devices. In otherembodiments, the intelligence device 120 can integrate with a remoterecording apparatus that can be physically or wirelessly connected tothe intelligence device 120. For example, the remote apparatus can be amicrophone integrated into a ring worn by the user 105 that iswirelessly connected to the user's mobile phone via Bluetooth or WiFiconnections.

The intelligence device 120 analyzes the conversation 110 to determineor calculate a verbal intelligence (VI) index. In some embodiment, theconversation 110 is analyzed according to VI factors such as Energy,Words, Inflection, Tone (e.g. pitch and sentiment), Rate, and/or thelike of words spoken. Pitch measures the pitch of the user's 105 voice.Energy measures the volume of the user's 105 voice. Words measures thenumber of words spoken, e.g. in whole conversation 110, in sentences,between third party responses, and/or the like. Inflection can measurethe modulation of the voice, Tone can measure intonation or the user'sgeneral character or attitude towards the words spoken. In someembodiments, Tone can measure pitch of the voice and the sentimentbehind the words spoken. In some embodiments, tone is the color ofspeech and inflection is defined as the color of speech text. Ratemeasures the speed the words spoken, e.g. in sentences, wholeconversation, start of conversation versus end of conversation, answersto questions, initial presentation, and/or the like.

In some embodiments, the intelligence device 120 captures rateconsistency and/or volume consistency (e.g. speeding up vs. slowingdown, increasing vs. decreasing volume). In other embodiments, theintelligence device 120 captures written, typed, or text communicationsto be analyzed individually and/or in conjunction with spokenconversations. In other embodiments, the intelligence device 120captures and analyzes braille, sign language, and/or the like.

The intelligence device 120 can analyze and rate or score some or eachVI data factor. The intelligence device 120 can generate an overall VIindex of the user by combining, aggregating, averaging, weightedaverage, mean, or each individual scoring of the data factors, e.g.energy, words, inflection, tone, rate, and/or the like.

The intelligence device 120 can process the conversation 110 using anautocorrelation algorithm to determine tone or intonations in theconversation 110 to facilitate determining the VI index. Theautocorrelation algorithm can be tuned according to machine learnedsettings to capture the intonations in the conversation 110. In someembodiments, the autocorrelation algorithm is based on at least one ofor a combination of Yin, Yaapt, Praat, and/or the like. The tunedsettings can affect Time Step, Pitch Floor (Hz), Very Accurate(Boolean), Pitch Ceiling (Hz), Silence Threshold, Voicing Threshold,Octave cost, Octave Jump-cost, Voiced/unvoiced cost, and/or the like.

In some embodiments, the intelligence device 120 factors demographicinformation of the third party 115 into the VI index. For example, athird party in Kansas City, Kans. provides feedback of their call asunsuccessful. The speech rate of the user was too fast, the voice wastoo loud, and the pitch rate is aggressive. The sentiment of the callwould be negative. Whereas, a third party in New York City, N.Y.provides feedback of their call with identical values as a successfulcall. The intelligence device 120 can automatically factor thedemographic information into the VI index.

In some embodiments, the intelligence device 120 can construct andutilize a predictive model to determine the VI index. The predictivemodel can be based on determined correlation data to correlate datafactors in previous conversations to feedback received about theprevious conversations. The previous conversations can be of the user,the third party, both, neither, and/or the like. The predictive modelcan be built using the correlations such that current and futureconversations need not utilize feedback for determining the VI index orother analytics.

The intelligence device 120 can acquire the feedback of the previousconversations by sending a survey or questionnaire to the third party115 and/or the user 105. The survey can include qualifying questionsabout the conversation such as how the conversation was presented,positive/negative sentiment, and/or the like. The intelligence device120 can receive the feedback as answers to the survey of the previousconversation. The survey can be sent to a third party's device.

The intelligence device 120 builds the prediction model by determiningcorrelations between the feedback and the data factors. The intelligencedevice 120 determines the correlation data based on the determinedcorrelations. In some embodiments, the intelligence device 120determines the correlation data by applying a machine learning structureto the feedback and the data factors to construct the correlation datainto the prediction model. The prediction model can receive theconversation 110 and use the prediction model on the conversation 110 todetermine the VI index. In some embodiments, the machine learning canevolve the prediction model over time as more conversations areconducted and analyzed to refine the prediction model for improvedresults of the VI index.

The intelligence device 120 can generate intelligent dashboards for theuser based on the analysis output. In some embodiments, the dashboardscan be interactive or static. In other embodiments, the dashboards arecustomized and/or personalized for the user. The dashboards display theanalysis output to the user, recommendations for improvement/increaseeffectiveness, playback of the recording of the conversation 110,interactive or annotated playback of the recording of the conversation110, a transcript of the conversation 110, and/or the like. The user 105can review the dashboards on a display of the intelligence device 120, aseparate computing device, and/or the like. In some embodiments, thedashboards can be emailed, texted, printed, push notification, and/orthe like to the user 105.

FIG. 2 illustrates an example component diagram of the intelligencedevice 120. The intelligence device 120 includes a capture component205. The capture component 205 can be a microphone and/or otherrecording apparatus. The capture component 205 can record theconversation 110 between the user 105 and the third party 115. Thecapture component 205 can store the recording of the conversation in alocal storage apparatus, remote or cloud solution, and/or the like. Insome embodiments, the capture component 205 can be integrated into theintelligence device 120. In other embodiments the capture component 205is a remote recording apparatus that can be physically or wirelesslyconnected to the intelligence device 120. For example, the capturecomponent 205 can be a microphone integrated into a ring worn by theuser that is wirelessly connected to the user's mobile phone viaBluetooth or WiFi connections.

The capture component 205 can capture data in multiple modes. Forexample, data can be captured using wearable, mobile device, microphone,and/or the like. In some embodiments, voice recognition can be used bythe capture component 205 to isolate different people during aconversation. In some embodiments, data can be captured via a smartphone, using an app and a device that will communicate to the phone froma device via Bluetooth technology. In some embodiments, data can becaptured via a telephone integrated into an organization's alreadyexisting phone system.

The intelligence device 120 includes a verbal analysis component 210.The verbal analytics component 210 receives the recording of theconversation 110 for analysis. The verbal analytics component 210analyzes the conversation 110 to determine or calculate a verbalintelligence (VI) index. The conversation 110 is analyzed to accordingVI factors such as Energy-Words-Tone-Rate of words spoken. Pitchmeasures the pitch of the user's 105 voice. Energy measures the volumeof the user's 105 voice. Words measures the number of words spoken, e.g.in whole conversation 110, in sentences, between third party responses,and/or the like. Tone can measure the user's general character orattitude towards the words spoken. Rate measures the speed the wordsspoken, e.g. in sentences, whole conversation, start of conversationversus end of conversation, answers to questions, initial presentation,and/or the like.

In some embodiments, rate consistency and/or volume consistency can becaptured (e.g. speeding up vs. slowing down, increasing vs. decreasingvolume). In other embodiments, written, typed, or text communicationscan be captured and analyzed individually and/or in conjunction withspoken conversations. In other embodiments, braille, sign language,and/or the like can be analyzed. The verbal analysis component 210 cananalyze and rate or score some or each VI data factor. The verbalanalysis component 210 can generate an overall VI index of the user bycombining, aggregating, averaging, weighted average, mean, or eachindividual scoring of the data factors.

In some embodiments, the verbal analysis component 210 can determine butnot limited to: Number of Words spoken within a specified time frame orconversation, Most common words used, Unique words used by the speaker,Verbal Intelligence Index, and/or the like.

In some embodiments, the verbal analysis component 210 can construct andutilize a predictive model to determine the VI index. The predictivemodel can be based on determined correlation data to correlate datafactors in previous conversations to feedback received about theprevious conversations. The previous conversations can be of the user,the third party, both, neither, and/or the like. The predictive modelcan be built using the correlations such that current and futureconversations need not utilize feedback for determining the VI index orother analytics.

The verbal analysis component 210 can acquire the feedback of theprevious conversations by sending a survey or questionnaire to the thirdparty 115 and/or the user 105. The survey can include qualifyingquestions about the conversation such as how the conversation waspresented, positive/negative sentiment, and/or the like. The verbalanalysis component 210 can receive the feedback as answers to the surveyof the previous conversation. The survey can be sent to a third party'sdevice.

The verbal analysis component 210 builds the prediction model bydetermining correlations between the feedback and the data factors. Theverbal analysis component 210 determines the correlation data based onthe determined correlations. In some embodiments, the verbal analysiscomponent 210 determines the correlation data by applying a machinelearning structure to the feedback and the data factors to construct thecorrelation data into the prediction model. The prediction model canreceive the conversation 110 and use the prediction model on theconversation 110 to determine the VI index.

The intelligence device 120 includes an interface component 215. Theinterface component 215 can receive the analysis output from the verbalanalysis component 210. The interface component 215 can generateintelligent dashboards for the user based on the analysis output. Insome embodiments, the dashboards can be interactive or static. In otherembodiments, the dashboards are customized and/or personalized for theuser. The dashboards display the analysis output to the user,recommendations for improvement/increase effectiveness, playback of therecording of the conversation 110, interactive or annotated playback ofthe recording of the conversation 110, a transcript of the conversation110, and/or the like. The user 105 can review the dashboards on adisplay of the intelligence device 120, a separate computing device,and/or the like. In some embodiments, the dashboards can be emailed,texted, push notification, and/or the like to the user 105.

In some embodiments, the interface component 215 can providerecommendations for conversation improvement within the dashboard. Therecommendations may be provided in generated dashboards in the app, asan email, presentation, and/or the like. In some embodiments, therecommendations may be made in real time in a view on a display on theuser's mobile device while a conversation is occurring. In otherembodiments, the recommendations may be made after conversation.

In an example embodiment, the VI index can be calculated using the belowalgorithm. It is appreciated that other algorithms may be used tocalculate the VI index. In some embodiments, the algorithm used tocalculate a user's VI Index is outlined below using a basic algebraicformula. The algorithm consists of three variables used to measure one'srelative VI. x=the total number of words the user speaks during aconversation. y=total number of trust inducing phrases used during aconversation by the user of the software. z=the score of one's toneduring a conversation. The score can range between 0-4, using the toneparameters defined below.

VI Index=100y/x+(z*100y/x). The VI index may be a weighted averageprogram. An example calculation can be:

Trust Phrases- 25% × 4 = 1 Volume- 25% × 2 = .5 Pitch-  25% × 3 = .75Rate of words 25% × 4 = 1 SUM — Total Score 3.25

The variable of having the weighted average for each category can bedetermined by the testing of 100 processing samples. The weighting canbe determined in multiple embodiments. In some embodiments, each userdecides on their own what the weighted average should be for each of thefour categories. In other embodiments, an initial weighting can use the25% weight for each category and after a predetermined amount of trialsamples the weighted average can be determined by the aggregate testresults of the trial samples. For example, an average of 100 trialsamples can be used for the point system, which rationalizes theweighted average for Trust Phrases is 30.77%, Volume is 15.38%, Pitch is23.08% and Rate of Words is 30.77%. The determined weights can be builtinto the software and the user does not have to make any subjectiveweighting decisions. In some embodiments, each user can select theirweighted average and then compare to the actual results of all trialsamples to learn how close or far apart the users are from the mean andthe average of trial samples. Using the above formula, a user's VI indexcan be between 1-10. In some embodiments, it is possible for a VI indexto be greater than 10. In some embodiments, rewards or views can begenerated according to the VI index. For example, a user can earn a goldstar when accomplishing a VI index greater than 8 or 10.

In some embodiments, tone measurement can be measured in multipleembodiments. To calculate the VI index, the intelligence device 120 canmeasure four features of a user or speaker's tone. Each feature canreceive a score of 1.33 or zero, and the scores of each feature can beadded to get a total tone score of 0-4. A score of one can be assignedto each feature if the speaker's conversation falls within the normalrange of human speech. A score of 0 can be assigned if one'sconversation does not fall within the normal range. For example, each ofthe features, and their ranges can be:

Rate of Speech

-   -   Less than 100 WPM=0    -   100-165 WPM=1.33    -   More than 165 WPM=0

Energy (e.g. Volume)

-   -   Average Decibels less than 50=0    -   50-65 Average Decibels=1.33    -   Average Decibels greater than 65=0

Pitch (e.g. Inflection)

-   -   Manic=0    -   Normal=1.34    -   Monotone=0

Trust Inducing and Negative Impact Phrases—For most valuable phrases(MVP), the intelligence device 120 can use “trust inducing” and“negative impact” phrases to measure a speaker's VI index. There aremultiple trust inducing phrases that can be counted and/or otherwisemeasured. Examples of trust inducing phrases can be, but not limited to:You and I (me), I (we) understand, I (we) care, I (we) trust yourjudgement, What are your thoughts, I'd (we′d) like to share, What do youthink, I (we) respect, Great idea, I'm (we're) available, Let's worktogether, How do you feel about, As your partner, Together, we can, I(we) sincerely believe, and/or the like. Examples of negative impactphrases can be, but not limited to: You can't, Bad idea, Wrong, Nochance, and/or the like.

The intelligence device 120 can be implemented for many markets and/oruse cases. For example, any user who uses a phone in a businessenvironment or whose success is determined by developing a relationshipwith the caller is affected. Some other use cases or applications caninclude:

Use Case 1: Specialized services and luxury items

Use Case 2: Services—Customer Service Representatives (CSR)

Use Case 3: Medical—Problem: Poor customer service that is difficult toquantify

Use Case 4: Financial—Problem: User often uninformed about servicesprovided; Solution: Increase trust level; Results can be measured

Use Case 5: IT—Problem: User generally unfamiliar with software;Solution: Sell more products and solutions; Results can be measured

Use Case 6: Recruiting/Staffing—Problem: A lot of falsifying andmisleading information traded between the recruiter and recruit;Solution: Ability to screen recruits for “non-skill related”competencies. Ability of recruiter to establish a level of trust withpotential recruit; Results can be measured

Use Case 7: Fundraising—Problem: Lack of planning, poor supportmaterials, not recruiting enough help; Solution: Ability of fundraiserto better sell the “mission” of the organization; Results can bemeasured

Use Case 8: Collection service—Problem: Aggressive dialing, Poorservices, Trust; Solution: Ability of the collector to establish awin-win situation with the debtor; Results can be measured

Use Case 9: Ride Sharing Applications—Problem: Perceived and realsecurity issues; Solution: Increased customer service through monitoringand coaching;

Use Case 10: Coaching—Problem: Unintended meanings of verbalcommunications; Solution: Create metrics to improve communication;

Use Case 11: Customer Service—Problem: Unskilled personnel, Lack ofempathy and too much automation; Solution: Create a “safe” atmospherewhere caller can openly discuss an issue(s); Results can be measured

Use Case 12: Teaching, tutoring, lessons

Use Case 13: Luxury (high end) Items Sales (e.g. Yachts, Planes,Automobiles and Real Estate)—Problem: Lack of trust with salesperson;Solution: Increased trust and communication skills through analysis andtraining; Results can be measured

Use Case 14: Existing telephone A.I. solutions—Problem: Lack of VIanalytics in the software; Solution: Ability of SaaS provider to includeupgrade for additional analytics; Results can be measured.

In some embodiments, the intelligence device 120 determines correlationusing captured inflections in text (Sentiment) and intonations in voice(Pitch) to identify common patterns within them to correlate tone andtext in conversations. The patterns are used to create the predictivemodel. Rising and falling intonations and circumflex pitch patterns canbe associated with certain words or phrases. The patterns can becorrelated with qualified call data to produce results that can then beused to predict possible future call outcomes based upon the call datacaptured in real-time. The analysis can assist callers with improvingthe overall outcome of the call.

FIGS. 3-8 depict illustrations for describing an example embodiment tocorrelate a conversation and sentiment. It is appreciated that otherembodiments are contemplated.

In an example embodiment, a conversation can be organized into a messagestructure to organize call data for analysis. In some embodiments, themessage structure is formatted in XML, JSON, another format, and/or thelike. FIG. 3 illustrates an example call message structure. The callmessage structure can be divided into three parts: a call tag, a callertag, and a recipient tag.

FIG. 4 depicts a call tag structure as part of the call messagestructure. The call tag structure includes:

CallID—a unique identifier used to define the call. This field will beused to uniquely access the call from within a database, such as a SQLdatabase.

CallType—used to identify the type of call conducted—sales, collections,financial, insurance, etc.

CallPurpose—used to define the purpose of the call—initial call,follow-up, quote, etc.

CallComments—used to capture comments made by the caller regarding thecall.

CallStartTime—the time the call began in universal time code (UTC)format.

CallEndTime—The time the call ended in UTC format.

Caller—This is the child element that contains all informationpertaining to the caller.

Recipient—This is the child element that contains all informationpertaining to the recipient of the call.

FIG. 5 depicts a caller tag structure part of the call messagestructure. The caller tag structure includes:

CallerID—A unique identifier used to define the caller. This field willbe used to uniquely identify the caller within the SQL Database.

CallText—This is a Binary large object (BLOB) object containing theentire contents of the caller's text during the call. This text iscreated by sending the call audio to a speech to text converter. Thistext may be further encrypted to prevent unauthorized access of the calltext due to legal purposes.

CallAudio—This is a BLOB object containing the entire contents of thecaller's audio during the call. In some embodiments, the audio can becreated by the 3rd party communication platform. This audio may befurther encrypted to prevent unauthorized access of the call audio dueto legal purposes.

PitchTrack—This is a BLOB object containing the entire contents of thecaller's pitch track during the call. In some embodiments, the pitchtrack can be created by an Audio Processing service of the intelligencedevice 120 using an autocorrelation algorithm. The pitch track may befurther encrypted to prevent unauthorized access of the call audio.

SpeechRate—This is the speech rate (in words per minute) the caller usedduring the duration of the call. The speech rate is created by the AudioProcessing service using a combination of data received by theCommunications platform and the Speech to Text converter.

Loudness—This is the loudness (RMS volume in decibels) of the caller'svoice during the duration of the call. The loudness is created by theAudio Processing service using data received by the Communicationsplatform.

Sentences—The Sentences Tag contains a collection of Sentencestructures, which are used to provide tone/text correlation informationfor data analysis.

Sentence—The Sentence Tag contains a collection of artifacts based upona spoken Sentence made by the Caller.

SentenceID—a unique identifier used to define the sentence. This fieldwill be used to uniquely access the sentence from within the SQLDatabase.

SentenceText—This is the actual text of the sentence that was spoken bythe caller. This data may be encrypted to prevent unauthorized access ofthe text due to legal purposes.

Sentiment—The Sentiment is the value calculated by the SentimentAnalysis engine based upon the text in the sentence created by thecaller.

TrustArtifacts—Trust Artifacts are words or phrases that are deemedvaluable to the quality and success of the conversation. These artifactsare captured so they can be qualified against the call.

TrustArtifact—The trust artifact used by the Caller during theconversation.

SpeechSegments—Every Sentence is made up of one or more speech segments.Speech segments are used to correlate pitch patterns within the speechsegments to the sentiment of the sentence spoken.

SpeechSegment—A speech segment contains both text and audio informationused for data analysis.

SegmentID—a unique identifier used to define the segment. This fieldwill be used to uniquely access the segment from within the SQLDatabase.

SegmentText—This is the actual text that was spoken by the caller duringthe speech segment. There may be zero or more words within the segmenttext.

PitchSegment—This is the actual pitch track segment associated with theidentification and classification of the Pitch Type.

PitchType—The pitch type is calculated by the Audio Processing serviceof the intelligence device 120 by analyzing the Pitch Track against thecaller's fundamental frequency. A standard deviation curve is plottedwith the caller's fundamental frequency used as the mean value.Deviations from the mean are segmented into categories defined by theadministrator of the system. For example, a mean value would have aPitch Type of “Normal”. Depending upon the scale, the administrator cancreate various levels of Pitch Types to correspond with the desireddefinition of the results displayed.

Qualification—The Qualification structure is used to define all of thequalifying information that will be used to correlate against the datacollected on the caller.

CallRating—This is used to provide a very simple call rating from theCaller when questionnaires are not implemented.

Questionnaires—One or more Questionnaires can be associated with a calldepending upon the business needs.

Questionnaire—This is a questionnaire completed by the caller.

QuestionnaireID—A unique identifier used to define the questionnaire.This field will be used to uniquely identify the questionnaire withinthe SQL Database.

QuestionnaireRating—This is rating generated based upon the valuescollected in the questions.

Questions—There can be one or more questions associated with aquestionnaire.

Question—This structure contains the key/value pairs associated with aquestion.

QuestionID—A unique identifier used to define the question. This fieldwill be used to uniquely identify the question within the SQL Database.

QuestionKey—This field contains the actual text of the question—i.e.)How would you rate the Recipients response to the questions?

QuestionValue—This field contains the actual answer to thequestion—i.e.) 5 or Excellent

Demographics—This structure contains demographic information regardingthe caller.

PlaceOfResidence—The caller's place of residence.

Age—The caller's age.

Sex—The caller's sex.

Ethnicity—The caller's ethnicity.

Income—The caller's income.

Education—The caller's education.

FIG. 6 depicts a recipient (i.e. third party) tag structure part of thecall message structure. The recipient structure includes:

RecipientID—A unique identifier used to define the recipient. This fieldwill be used to uniquely identify the recipient within the SQL Database.

CallText—This is a BLOB object containing the entire contents of therecipient's text during the call. This text is created by sending thecall audio to a speech to text converter. This text may be furtherencrypted to prevent unauthorized access of the call text.

CallAudio—This is a BLOB object containing the entire contents of therecipient's audio during the call. In some embodiments, the audio iscreated by the 3rd party communication platform. This audio may befurther encrypted to prevent unauthorized access of the call audio dueto legal purposes.

PitchTrack—This is a BLOB object containing the entire contents of therecipient's pitch track during the call. This pitch track is created bythe Audio Processing service of the intelligence device 120 using theautocorrelation algorithm. This pitch track may be further encrypted toprevent unauthorized access of the call audio due to legal purposes.

SpeechRate—This is the speech rate (in words per minute) the recipientused during the duration of the call. The speech rate is created by theAudio Processing service using a combination of data received by theCommunications platform and the Speech to Text converter.

Loudness—This is the loudness (RMS volume in decibels) of therecipient's voice during the duration of the call. The loudness iscreated by the Audio Processing service using data received by theCommunications platform.

Sentences—The Sentences Tag contains a collection of Sentencestructures, which are used to provide tone/text correlation informationfor data analysis.

Sentence—The Sentence Tag contains a collection of artifacts based upona spoken Sentence made by the Recipient.

SentenceID—a unique identifier used to define the sentence. This fieldwill be used to uniquely access the sentence from within the SQLDatabase.

SentenceText—This is the actual text of the sentence that was spoken bythe recipient. This data may be encrypted to prevent unauthorized accessof the text due to legal purposes.

Sentiment—The Sentiment is the value calculated by the SentimentAnalysis engine based upon the text in the sentence created by therecipient.

SpeechSegments—Every Sentence is made up of one or more speech segments.Speech segments are used to correlate pitch patterns within the speechsegments to the sentiment of the sentence spoken.

SpeechSegment—A speech segment contains both text and audio informationused for data analysis.

SegmentID—a unique identifier used to define the segment. This fieldwill be used to uniquely access the segment from within the SQLDatabase.

SegmentText—This is the actual text that was spoken by the recipientduring the speech segment. There may be zero or more words within thesegment text.

PitchSegment—This is the actual pitch track segment associated with theidentification and classification of the Pitch Type.

PitchType—The pitch type is calculated by the intelligence device 120 byanalyzing the Pitch Track against the recipient's fundamental frequency.A standard deviation curve is plotted with the recipient's fundamentalfrequency used as the mean value. Deviations from the mean are segmentedinto categories defined by the administrator of the system. For example,a mean value would have a Pitch Type of “Normal”. Depending upon thescale, the administrator can create various levels of Pitch Types tocorrespond with the desired definition of the results displayed.

Qualification—The Qualification structure is used to define all of thequalifying information that will be used to correlate against the datacollected on the recipient.

CallRating—This is used to provide a very simple call rating from theRecipient when questionnaires are not implemented.

Questionnaires—One or more Questionnaires can be associated with a calldepending upon the business needs.

Questionnaire—This is a questionnaire completed by the recipient.

QuestionnaireID—A unique identifier used to define the questionnaire.This field will be used to uniquely identify the questionnaire withinthe SQL Database.

QuestionnaireRating—This is rating generated based upon the valuescollected in the questions.

Questions—There can be one or more questions associated with aquestionnaire.

Question—This structure contains the key/value pairs associated with aquestion.

QuestionID—A unique identifier used to define the question. This fieldwill be used to uniquely identify the question within the SQL Database.

QuestionKey—This field contains the actual text of the question—i.e.)How would you rate the Recipients response to the questions?

QuestionValue—This field contains the actual answer to thequestion—i.e.) 5 or Excellent

Demographics—This structure contains demographic information regardingthe recipient.

PlaceOfResidence—The recipient's place of residence.

Age—The recipient's age.

Sex—The recipient's sex.

Ethnicity—The recipient's ethnicity.

Income—The recipient's income.

Education—The recipient's education.

In an example embodiment, the audio from the conversation 110 isprocessed for correlation and/or generating a VI index. The audioprocessing is used to determine call data for analysis. The call datacan include at least one of: The Caller's audio stream, The Recipient'saudio stream, The Caller's audio stream converted to text, TheRecipient's audio stream converted to text, The Caller's Pitch Track,The Recipient's Pitch Track, The Caller's Speech Rate in words perminute, The Recipient's Speech Rate in words per minute, The Caller'sLoudness (RMS) in decibels, The Recipient's Loudness (RMS) in decibels,The Caller's Sentiment, The Recipient's Sentiment, The Caller's TrustArtifacts, and/or the like.

In an example embodiment, the conversation audio is an audio stream overa telephone. In some embodiments, the audio stream is provided by aCommunications Platform that is capable of making and receiving phonecalls, text messages, and other communication functions via an API layerwith the intelligence device 120. The intelligence device 120 canreceive a stream of audio from the Communication Platform. The audio forthe Caller and Recipient is buffered across two distinct audio channelsand is stored as in respective locations within the call message, i.e.Caller/CallAudio and Recipient/CallAudio.

In the example embodiment, the intelligence device 120 can performspeech to text processing of the conversation 110. The Speech to Textprocessing facilitates sentiment analysis, identify trust data, andpitch emphasis on sentence structures deemed successful or unsuccessful.In some embodiments, the Speech to Text processing is performed by aSpeech to Text provider. The intelligence device 120 can submit theaudio stream received by the Communications Platform to the Speech toText provider, which in turn, will provide a text version of theconversation 110. The text can include punctuation such as periods andquestion marks based upon the pitch analysis and silence sampling of theconversation 110. The text can be store in respective locations withinthe call message, i.e. Caller/CallText and Recipient/CallText. For eachsentence created by the Speech to Text processor, the sentence will bestored in the <SentenceText> element of the respective Caller orRecipient <Sentence> segment. Each sentence will be given a uniquesentence identifier, which is stored in the <SentenceID> element. Theidentifier can be used to provide the ordering and processing of theSentences.

In the example embodiment, the intelligence device 120 includes PitchTrack processing that identifies pitch changes in voices when speaking.FIG. 7 illustrates a Pitch Track view and analysis of deviations from abaseline pitch to determine sentiment. The Pitch Track for therespective Caller and Recipient is stored as respective locations withinthe CALL message, i.e. Caller/PitchTrack and Recipient/PitchTrack. TheBaseline Pitch is the mean pitch frequency seen throughout theconversation. It represents the fundamental frequency (f0) of the calleror recipient. Any deviations from this baseline value can be identifiedby scaling the deviations from the mean and assigning definitions to thescaled values as in the example above. In some embodiments, customdefinable definitions can be set. These labels on the scale are thenreported in the Call Message <PitchType> element within the<SpeechSegment> section.

In the example embodiment, the intelligence device 120 determine speechrate of the conversation 110. The Speech rate is determined byidentifying the number of words spoken within a specific period of time(e.g. words per minute). The Speech Rate for the respective Caller andRecipient is stored in respective locations within the call message,i.e. Caller/SpeechRate and Recipient/SpeechRate.

In the example embodiment, the intelligence device 120 determinesloudness of the conversation. The loudness is determined by identifyingvoice amplification (RMS in decibels) over the period of the call forthe Caller and the Recipient. The Loudness for the respective Caller andRecipient is stored in respective locations within the CALL message,i.e. Caller/Loudness and Recipient/Loudness.

In the example embodiment, the intelligence device 120 can determinesentence structure of sentences in the conversation 110. The Sentencestructure can provide correlation between text, tone, and qualifiedattributes.

The intelligence device 120 can perform Sentiment Analysis on wholesentences. The sentences contain individual words or phrases thatcontain pitch and trust artifacts. Correlation of sentences tosuccessful conversations are identified by the intelligence device 120and distinguish between successful and unsuccessful sentences to findcorrelations between sentences and speech segment artifacts within thesentences. A sentence includes one or more speech segments. Each speechsegment can be identified by a brief pause between words. A speechsegment may contain one or more words. Each speech segment will includea portion of the pitch track described above.

FIG. 8 depicts a synchronization of the pitch track and the text tospeech segment. The intelligence device 120 synchronizes theconversation 110 and the pitch track and the text. The synchronizationprovides the ability to identify speech segments within sentences. Fromthe analysis, the intelligence device 120 can identify the specific textspoken within a speech segment and the pitch track segment identifiedwithin the speech segment. As mentioned earlier, the intelligence device120 analyzes the Pitch Track against the recipient's fundamentalfrequency. A standard deviation curve can plotted with the recipient'sfundamental frequency used as the mean value. The intelligence device120 segments deviations from the mean into defined categories. Forexample, a mean value could have a Pitch Type of “Normal”. Dependingupon the scale, the intelligence device 120 can determine differentlevels of Pitch Types to correspond with a definition of the resultsdisplayed. The mean or deviations for a given speech segment aredetermined by the intelligence device 120 and applied to the speechsegment structure in the CALL message.

The SegmentText for the respective Caller and Recipient is stored inrespective locations within the CALL message, i.e.

Caller/Sentences/Sentence/SpeechSegments/SpeechSegment/SegmentText andRecipient/Sentences/Sentence/SpeechSegments/SpeechSegment/SegmentText.

The PitchSegment for the respective Caller and Recipient is stored inrespective locations within the CALL message, i.e.

Caller/Sentences/Sentence/SpeechSegments/SpeechSegment/PitchSegment andRecipient/Sentences/Sentence/SpeechSegments/SpeechSegment/PitchSegment.

The PitchType for the respective Caller and Recipient is stored inrespective locations within the CALL message, i.e.Caller/Sentences/Sentence/SpeechSegments/SpeechSegment/PitchType andRecipient/Sentences/Sentence/SpeechSegments/SpeechSegment/PitchType.

The intelligence device 120 performs sentiment analysis. SentimentAnalysis is the process of mining text for clues about positive ornegative sentiment. Sentiment labels (such as “negative”, “neutral” and“positive”) are defined based on the highest confidence score found bythe analysis at a sentence and document-level. The intelligence device120 returns confidence scores between 0 and 1 for each conversation &sentences within it for positive, neutral and negative sentiment. Theintelligence device 120 conducts opinion mining. The opinion mining canbe Aspect-based Sentiment Analysis in Natural Language Processing (NLP).The intelligence device 120 provides granular information about theopinions related to aspects (such as the attributes of products orservices) in text of the conversation. The intelligence device 120facilitates processing of Sentiment Analysis. The intelligence device120 receives either the Call Message or a reference to the Call Messageto access the Call Text for the Caller and Recipient from the Callmessage. The intelligence device 120 analyzes the Caller and Recipientsentence structures. Each sentence that is processed by the intelligencedevice 120 and analyzed and its corresponding sentiment value is storedin the <Sentiment> element of the <Sentence> being processed. Forexample, the Caller has 5 sentence structures located in the <Sentences>node. Each sentence's <SentenceText> data is sent to the intelligencedevice 120 using the <SentenceID> as the order in which they areprocessed. When the intelligence device 120 returns the sentiment valuefor a given sentence, the sentiment value is stored in the Sentence's<Sentiment> tag.

FIG. 9 depicts an exemplary embodiment of a live view or real timedashboard generated on a computer screen in a cloud solution for a userspeaking with a customer. In some embodiments, the user 105 can use aheadset capture device attached to a computer terminal device 910. Thecapture device and/or the computer terminal device 910 can capture theuser's voice and recognize the conversation. The conversation can beprovided to a cloud solution 920 for analysis to determine a live VIindex dashboard 930. The live VI index dashboard 930 can be providedfrom the cloud solution 920 to the computer terminal device 910 anddisplayed to the user 105.

FIG. 10 depicts an exemplary embodiment of a wearable device 1010 as acapture component that is wirelessly (e.g. Bluetooth) connected to auser's mobile phone 1020. The mobile phone 1020 can utilize a cloudsolution 1030 for analysis and generating a dashboard view(s) 1030. Thedashboard views 1030 and analysis can be pushed to the mobile phone 1020and/or a user's personal computing device 1040 for displaying to theuser.

FIG. 11 depicts an exemplary embodiment of a VI index meter view. Theview can be displayed on the intelligence device 120 to the user 105after the call or in real or near real time during the call.

The aforementioned systems, architectures, platforms, environments, orthe like have been described with respect to interaction between severalcomponents. It should be appreciated that such systems and componentscan include those components or sub-components specified therein, someof the specified components or sub-components, and/or additionalcomponents. Sub-components could also be implemented as componentscommunicatively coupled to other components rather than included withinparent components. Further yet, one or more components and/orsub-components may be combined into a single component to provideaggregate functionality. Communication between systems, componentsand/or sub-components can be accomplished in accordance with either apush and/or pull control model. The components may also interact withone or more other components not specifically described herein for sakeof brevity, but known by those of skill in the art.

Furthermore, various portions of the disclosed systems above and methodsbelow can include or employ artificial intelligence, machine learning,or knowledge or rule-based components, sub-components, processes, means,methodologies, or mechanisms (e.g., support vector machines, neuralnetworks, expert systems, Bayesian belief networks, fuzzy logic, datafusion engines, classifiers . . . ). Among other things, such componentscan automate certain mechanisms or processes performed thereby to makeportions of the systems and methods more adaptive as well as efficientand intelligent. By way of example, and not limitation, such mechanismscan be utilized by the intelligence device 120 for verbal analytics.

In view of the exemplary systems described above, methods that may beimplemented in accordance with the disclosed subject matter will bebetter appreciated with reference to flow chart diagram of FIG. 12.While for purposes of simplicity of explanation, the methods are shownand described as a series of blocks, it is to be understood andappreciated that the disclosed subject matter is not limited by theorder of the blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Moreover, not all illustrated blocks may be required toimplement the methods described hereinafter. Further, each block orcombination of blocks can be implemented by computer programinstructions that can be provided to a processor to produce a machine,such that the instructions executing on the processor create a means forimplementing functions specified by a flow chart block.

FIG. 12 illustrates a method 1200 for verbal language analysis. At 1205,a conversation between a user and a third party is captured. Forexample, an intelligence device includes a microphone that records theconversation. At 1210, the conversation is analyzed according to verballanguage analysis for the user. The conversation is analyzed forindividual conversation data factors. At 1215, a verbal intelligenceindex is calculated from the analysis of the conversation factors. Theverbal intelligence index is a score metric based on at least one ofenergy, words, inflection, tone (e.g. pitch and sentiment), or rate. Insome embodiments, the verbal intelligence index can be normalized to ascore out of 100. At 1220, a dashboard that provides analytics to theuser for review is generated. The dashboard conveys analytics, verbalintelligence index, recommendations for improvement, and/or the like.The dashboard can be interactive and provide playback of theconversation or highlights of parts of the conversation that could havebeen improved. At 1225, the dashboard is provided to the user. Thedashboard can be provided to the user on the intelligence device via aninterface. The dashboard may also be provided to the user via anotification, email, text, alert, and/or the like and viewed on anydevice by the user.

A method, comprising: capturing a conversation between a user and athird party; analyzing the conversation according to verbal languageanalysis for the user; and generating a dashboard that providesanalytics to the user for review.

A system, comprising: one or more processors; a memory storing one ormore instructions that, when executed by the one or more processors,cause the one or more processors to perform a method comprising: capturea conversation between a user and a third party; analyze theconversation according to verbal language analysis for the user; andgenerate a dashboard that provides analytics to the user for review.

A computer readable medium having instructions to control one or moreprocessors configured to: capture a conversation between a user and athird party; analyze the conversation according to verbal languageanalysis for the user; and generate a dashboard that provides analyticsto the user for review.

As used herein, the terms “component” and “system,” as well as variousforms thereof (e.g., components, systems, sub-systems . . . ) areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a component may be, but is not limited to being,a process running on a processor, a processor, an object, an instance,an executable, a thread of execution, a program, and/or a computer. Byway of illustration, both an application running on a computer and thecomputer can be a component. One or more components may reside within aprocess and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers.

The conjunction “or” as used in this description and appended claims isintended to mean an inclusive “or” rather than an exclusive “or,” unlessotherwise specified or clear from context. In other words, “‘X’ or ‘Y’”is intended to mean any inclusive permutations of “X” and “Y.” Forexample, if “‘A’ employs ‘X,’” “‘A employs ‘Y,’” or “‘A’ employs both‘X’ and ‘Y,’” then “‘A’ employs ‘X’ or ‘Y’” is satisfied under any ofthe foregoing instances.

Furthermore, to the extent that the terms “includes,” “contains,” “has,”“having” or variations in form thereof are used in either the detaileddescription or the claims, such terms are intended to be inclusive in amanner similar to the term “comprising” as “comprising” is interpretedwhen employed as a transitional word in a claim.

To provide a context for the disclosed subject matter, FIG. 13 as wellas the following discussion are intended to provide a brief, generaldescription of a suitable environment in which various aspects of thedisclosed subject matter can be implemented. The suitable environment,however, is solely an example and is not intended to suggest anylimitation as to scope of use or functionality.

While the above disclosed system and methods can be described in thegeneral context of computer-executable instructions of a program thatruns on one or more computers, those skilled in the art will recognizethat aspects can also be implemented in combination with other programmodules or the like. Generally, program modules include routines,programs, components, data structures, among other things that performparticular tasks and/or implement particular abstract data types.Moreover, those skilled in the art will appreciate that the abovesystems and methods can be practiced with various computer systemconfigurations, including single-processor, multi-processor, multi-coreprocessor, quantum processor, or multi-quantum parallel processorcomputer systems, mini-computing devices, server computers, as well aspersonal computers, hand-held computing devices (e.g., personal digitalassistant (PDA), smart phone, tablet, watch . . . ),microprocessor-based or programmable consumer or industrial electronics,and the like. Aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network. However, some, if not allaspects, of the disclosed subject matter can be practiced on stand-alonecomputers. In a distributed computing environment, program modules maybe located in one or both of local and remote memory devices.

With reference to FIG. 13, illustrated is an example computing device1300 (e.g., desktop, laptop, tablet, watch, server, hand-held,programmable consumer or industrial electronics, set-top box, gamesystem, compute node . . . ). The computing device 1300 includes one ormore processor(s) 1310, memory 1320, system bus 1330, storage device(s)1340, input device(s) 1350, output device(s) 1360, and communicationsconnection(s) 1370. The system bus 1330 communicatively couples at leastthe above system constituents. However, the computing device 1300, inits simplest form, can include one or more processors 1310 coupled tomemory 1320, wherein the one or more processors 1310 execute variouscomputer executable actions, instructions, and or components stored inthe memory 1320.

The processor(s) 1310 can be implemented with a general-purposeprocessor, a digital signal processor (DSP), an application specificintegrated circuit (ASIC), a field programmable gate array (FPGA) orother programmable logic device, discrete gate or transistor logic,discrete hardware components, or any combination thereof designed toperform the functions described herein. A general-purpose processor maybe a microprocessor, but in the alternative, the processor may be anyprocessor, controller, microcontroller, or state machine. Theprocessor(s) 1310 may also be implemented as a combination of computingdevices, for example a combination of a DSP and a microprocessor, aplurality of microprocessors, multi-core processors, one or moremicroprocessors in conjunction with a DSP core, or any other suchconfiguration. In one embodiment, the processor(s) 1310 can be agraphics processor unit (GPU) that performs calculations with respect todigital image processing and computer graphics.

The computing device 1300 can include or otherwise interact with avariety of computer-readable media to facilitate control of thecomputing device to implement one or more aspects of the disclosedsubject matter. The computer-readable media can be any available mediathat accessible to the computing device 1300 and includes volatile andnonvolatile media, and removable and non-removable media.Computer-readable media can comprise two distinct and mutually exclusivetypes, namely storage media and communication media.

Storage media includes volatile and nonvolatile, removable, andnon-removable media implemented in any method or technology for storageof information such as computer-readable instructions, data structures,program modules, or other data. Storage media includes storage devicessuch as memory devices (e.g., random access memory (RAM), read-onlymemory (ROM), electrically erasable programmable read-only memory(EEPROM) . . . ), magnetic storage devices (e.g., hard disk, floppydisk, cassettes, tape . . . ), optical disks (e.g., compact disk (CD),digital versatile disk (DVD) . . . ), and solid state devices (e.g.,solid state drive (SSD), flash memory drive (e.g., card, stick, keydrive . . . ) . . . ), or any other like mediums that store, as opposedto transmit or communicate, the desired information accessible by thecomputing device 1300. Accordingly, storage media excludes modulateddata signals as well as that described with respect to communicationmedia.

Communication media embodies computer-readable instructions, datastructures, program modules, or other data in a modulated data signalsuch as a carrier wave or other transport mechanism and includes anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, radio frequency (RF), infrared and other wireless media.

The memory 1320 and storage device(s) 1340 are examples ofcomputer-readable storage media. Depending on the configuration and typeof computing device, the memory 1320 may be volatile (e.g., randomaccess memory (RAM)), non-volatile (e.g., read only memory (ROM), flashmemory . . . ) or some combination of the two. By way of example, thebasic input/output system (BIOS), including basic routines to transferinformation between elements within the computing device 1300, such asduring start-up, can be stored in nonvolatile memory, while volatilememory can act as external cache memory to facilitate processing by theprocessor(s) 1310, among other things.

The storage device(s) 1340 include removable/non-removable,volatile/non-volatile storage media for storage of vast amounts of datarelative to the memory 1320. For example, storage device(s) 1340include, but are not limited to, one or more devices such as a magneticor optical disk drive, floppy disk drive, flash memory, solid-statedrive, or memory stick.

Memory 1320 and storage device(s) 1340 can include, or have storedtherein, operating system 1380, one or more applications 1386, one ormore program modules 1384, and data 1382. The operating system 1380 actsto control and allocate resources of the computing device 1300.Applications 1386 include one or both of system and application softwareand can exploit management of resources by the operating system 1380through program modules 1384 and data 1382 stored in the memory 1320and/or storage device(s) 1340 to perform one or more actions.Accordingly, applications 1386 can turn a general-purpose computer 1300into a specialized machine in accordance with the logic providedthereby.

All or portions of the disclosed subject matter can be implemented usingstandard programming and/or engineering techniques to produce software,firmware, hardware, or any combination thereof to control the computingdevice 1300 to realize the disclosed functionality. By way of exampleand not limitation, all or portions of the intelligence device 120 canbe, or form part of, the application 1386, and include one or moremodules 1384 and data 1382 stored in memory and/or storage device(s)1340 whose functionality can be realized when executed by one or moreprocessor(s) 1310.

In accordance with one particular embodiment, the processor(s) 1310 cancorrespond to a system on a chip (SOC) or like architecture including,or in other words integrating, both hardware and software on a singleintegrated circuit substrate. Here, the processor(s) 1310 can includeone or more processors as well as memory at least similar to theprocessor(s) 1310 and memory 1320, among other things. Conventionalprocessors include a minimal amount of hardware and software and relyextensively on external hardware and software. By contrast, an SOCimplementation of processor is more powerful, as it embeds hardware andsoftware therein that enable particular functionality with minimal or noreliance on external hardware and software. For example, theintelligence device 120 and/or functionality associated therewith can beembedded within hardware in a SOC architecture.

The input device(s) 1350 and output device(s) 1360 can becommunicatively coupled to the computing device 1300. By way of example,the input device(s) 1350 can include a pointing device (e.g., mouse,trackball, stylus, pen, touch pad . . . ), keyboard, joystick,microphone, voice user interface system, camera, motion sensor, and aglobal positioning satellite (GPS) receiver and transmitter, among otherthings. The output device(s) 1360, by way of example, can correspond toa display device (e.g., liquid crystal display (LCD), light emittingdiode (LED), plasma, organic light-emitting diode display (OLED) . . .), speakers, voice user interface system, printer, and vibration motor,among other things. The input device(s) 1350 and output device(s) 1360can be connected to the computing device 1300 by way of wired connection(e.g., bus), wireless connection (e.g., Wi-Fi, Bluetooth . . . ), or acombination thereof.

The computing device 1300 can also include communication connection(s)1370 to enable communication with at least a second computing device1302 by means of a network 1390. The communication connection(s) 1370can include wired or wireless communication mechanisms to supportnetwork communication. The network 1390 can correspond to a local areanetwork (LAN) or a wide area network (WAN) such as the Internet. Thesecond computing device 1302 can be another processor-based device withwhich the computing device 1300 can interact. For example, the computingdevice 1300 can correspond to a server that executes functionality ofintelligence device 120, and the second computing device 1302 can be auser device that communications and interacts with the computing device1300.

What has been described above includes examples of aspects of theclaimed subject matter. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but one of ordinary skill in theart may recognize that many further combinations and permutations of thedisclosed subject matter are possible. Accordingly, the disclosedsubject matter is intended to embrace all such alterations,modifications, and variations that fall within the spirit and scope ofthe appended claims.

What is claimed is:
 1. A method, comprising: capturing a conversationbetween a user and a third party; analyzing the conversation accordingto verbal language analysis for the user; and generating a dashboardthat provides analytics to the user for review.
 2. The method of claim1, comprising: wherein the verbal language analysis includes calculatinga verbal intelligence index based on data factors.
 3. The method ofclaim 2, comprising: wherein the verbal intelligence index is a scoremetric based on the data factors, the data factors including at leastone of energy, words, inflection, tone, or rate.
 4. The method of claim2, comprising: wherein the verbal intelligence index is based in part oncorrelation data, wherein the correlation data is based in party onfeedback provided from previous conversations of the user.
 5. The methodof claim 4, comprising: acquiring the feedback from the previousconversations, wherein the acquiring comprises: receiving the feedbackfrom the user and a recipient of the previous conversation; determiningcorrelations between the feedback and the data factors; and constructingthe correlation data based on the determined correlations.
 6. The methodof claim 5, comprising: determining a prediction model based on thecorrelation data; and determining the verbal intelligence index based onthe prediction model.
 7. The method of claim 6, wherein constructing thecorrelation data comprises: applying a machine learning structure to thefeedback and the data factors to construct the correlation data.
 8. Themethod of claim 2, wherein determining the verbal intelligence indexcomprises: determining demographic information of the third party; anddetermining the verbal intelligence index based on the demographicinformation.
 9. The method of claim 2, comprising: wherein determiningthe verbal intelligence index is based on an autocorrelating algorithm,wherein the autocorrelating algorithm is tuned according to machinelearned settings to capture intonations.
 10. A system, comprising: oneor more processors; a memory storing one or more instructions that, whenexecuted by the one or more processors, cause the one or more processorsto perform a method comprising: capture a conversation between a userand a third party; analyze the conversation according to verbal languageanalysis for the user; and generate a dashboard that provides analyticsto the user for review.
 11. The system of claim 10, the methodcomprising: wherein the verbal language analysis includes calculating averbal intelligence index based on data factors.
 12. The system of claim11, the method comprising: wherein the verbal intelligence index is ascore metric based on the data factors, the data factors including atleast one of energy, words, inflection, tone, or rate.
 13. The system ofclaim 11, the method comprising: wherein the verbal intelligence indexis based in part on correlation data, wherein the correlation data isbased in party on feedback provided from previous conversations of theuser.
 14. The system of claim 13, the method comprising: acquiring thefeedback from the previous conversations, wherein the acquiringcomprises: receiving the feedback from the user and a recipient of theprevious conversation; determining correlations between the feedback andthe data factors; and constructing the correlation data based on thedetermined correlations.
 15. The system of claim 14, the methodcomprising: determining a prediction model based on the correlationdata; and determining the verbal intelligence index based on theprediction model.
 16. The system of claim 15, wherein constructing thecorrelation data comprises: applying a machine learning structure to thefeedback and the data factors to construct the correlation data.
 17. Thesystem of claim 11, wherein determining the verbal intelligence indexcomprises: determining demographic information of the third party; anddetermining the verbal intelligence index based on the demographicinformation.
 18. The system of claim 11, the method comprising: whereindetermining the verbal intelligence index is based on an autocorrelatingalgorithm, wherein the autocorrelating algorithm is tuned according tomachine learned settings to capture intonations.
 19. A computer readablemedium having instructions to control one or more processors configuredto: capture a conversation between a user and a third party; analyze theconversation according to verbal language analysis for the user; andgenerate a dashboard that provides analytics to the user for review. 20.The computer readable medium of claim 19, wherein the one or moreprocessors are further configured to: wherein the verbal languageanalysis includes calculating a verbal intelligence index based on datafactors, and wherein the verbal intelligence index is a score metricbased on the data factors, the data factors including at least one ofenergy, words, inflection, tone, or rate.